TruaceTracing the truth around AITuesday, September 15, 2026
The Index

What the evidence says.What the public feels.

Ranks distinct AI gain and problem claims from the published record. Scores reward impact, independent source strength, scale, confidence, and recency.

1,418 results
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AI gains · 787

77
GainCrime· Stable· Evidence: Moderate (1 source)

Open-source MCP servers demonstrated strong health metrics despite rapid adoption with SDK downloads surpassing twenty five million per week.

In a first large-scale empirical study published May 2026, researchers examined 1,899 open-source Model Context Protocol servers, the standard introduced by Anthropic in late 2024 to unify tool calling for Foundation Models. Using health metrics and a combined general and MCP-specific scanner, they measured adoption signals and code quality across the ecosystem.

Impact 30%49
Evidence 25%95
Scale 20%85
Confidence 15%87
Recency 10%88

Updated Jul 13, 2026 · TRV-2026-0137

77
GainHealth· Stable· Evidence: Moderate (1 source)

A Random Forest model trained on linguistic, emotional, cognitive, behavioral and temporal features from Weibo posts predicted Self-Rating Anxiety Scale scores among consenting Chinese college students with R2 0.77 on the test set.

Researchers surveyed college students in China with the Self-Rating Anxiety Scale and, with informed consent, analyzed their public Weibo posts. Using multi-dimensional features, a Random Forest model predicted anxiety scores within the study sample, achieving the best test performance among four models tested.

Impact 30%49
Evidence 25%95
Scale 20%85
Confidence 15%87
Recency 10%88

Updated Jul 13, 2026 · TRV-2026-0121

76
GainLabor· Stable· Evidence: High (5 sources)

Integration of blockchain and AI in accounting improves audit quality, enhances transparency and data reliability, and reduces operational costs while automating routine tasks.

Published May 8 2026, this peer-reviewed study examined how blockchain and artificial intelligence are changing accounting, auditing, financial reporting, and accounting education. Using questionnaires from Chartered Accountants and audit firm professionals, it found that blockchain's immutable transparent ledger and AI automation can improve data reliability, enable real-time auditing, and reduce fraud and operational costs.

Impact 30%49
Evidence 25%100
Scale 20%60
Confidence 15%100
Recency 10%88

Updated Jul 13, 2026 · TRV-2026-0169

AI problems · 631

73
ProblemHealth· Stable· Evidence: Moderate (1 source)

Clinical use of AI is limited by hallucinations, algorithmic bias, data protection requirements, and regulatory considerations that require continuous human oversight.

A July 2026 review in Die Urologie describes artificial intelligence moving from research into everyday clinical practice and hospital care, with generative AI and large language models now used alongside established image-analysis tools for documentation, knowledge management, patient communication, and workflow optimization, plus AI-assisted radiological and pathological interpretation and risk stratification.

Impact 30%49
Evidence 25%95
Scale 20%60
Confidence 15%87
Recency 10%91

Updated Jul 31, 2026 · TRV-2026-0601

73
ProblemClimate· Stable· Evidence: Moderate (1 source)

Implementation of AI in waste management faces challenges due to financial and personnel constraints.

A peer-reviewed study published October 4, 2025 examined AI for municipal waste management in Industry 4.0. Based on a 2024 online survey of 78 respondents mainly from Europe with experience or interest in AI, logistics, and ecology, authors reported that 78% saw AI reducing waste management costs, 59% saw greatest benefits in sorting and recycling, and 51% saw effectiveness in optimizing collection routes.

Impact 30%49
Evidence 25%95
Scale 20%60
Confidence 15%87
Recency 10%91

Updated Jul 29, 2026 · TRV-2026-0585

73
ProblemHealth· Stable· Evidence: Moderate (1 source)

Digital mental health tools are hampered by engagement challenges, industry setbacks, methodological critiques, and gaps in evidence and scaling that limit real-world applicability.

As of May 2025, this review in World Psychiatry examined how smartphone apps, virtual reality, and generative AI including large language models are being applied to mental health, evaluating evidence across well-being, depression, anxiety, schizophrenia, eating disorders and substance use, and outlining advances in digital phenotyping and generative outputs.

Impact 30%49
Evidence 25%95
Scale 20%60
Confidence 15%87
Recency 10%90

Updated Jul 24, 2026 · TRV-2026-0523

73
ProblemHealth· Stable· Evidence: Moderate (1 source)

Across 29 standardized clinical vignettes, all 21 tested LLMs failed differential diagnosis in over 80% of cases, indicating they have not achieved the reasoning needed for safe clinical deployment.

Researchers evaluated 21 off-the-shelf large language models, including GPT-5, Claude 4.5 Opus, Gemini 3.0 and Grok 4, on 29 standardized MSD Manual clinical vignettes representing 16,254 responses scored by medical students. Using the PrIME-LLM composite across differential diagnosis, diagnostic testing, final diagnosis, management, and miscellaneous reasoning, scores ranged from 0.64 to 0.78.

Impact 30%69
Evidence 25%95
Scale 20%35
Confidence 15%87
Recency 10%88

Updated Jul 13, 2026 · TRV-2026-0145

Recomputed live from the record · Sep 15, 2026, 1:40 PM